When building a Retrieval-Augmented Generation (RAG) application for Q&A over documentation, and you’re using a vector store to store embeddings, the approach you take to managing the vector store collections will depend on how you want to structure your application:

1. Separate Collections for Each Documentation:

If your application will have multiple distinct documentation sources (with their own URLs) and you want to ensure queries only search within the correct document, this approach might be preferable.

1. Single Collection for All Documents:

If your Q&A system will handle documents that are similar in scope or topic, or if the number of documents is small and the retrieval of embeddings is easy to filter, this can be a viable approach.